Discrete element-based pea gravel reclamation process simulation method and system and application

Abstract

The invention relates to the technical field of geotechnical engineering grade tunnel engineering, and provides a pea gravel hydraulic reclamation process simulation method and system based on discrete elements and application. The method comprises the following steps: S1, determining size parameters of a shield tunnel, macromechanical parameters of duct pieces and surrounding rocks, a hydraulic reclamation position and hydraulic reclamation pressure; s2, determining particle size distribution of pea gravel particles and mesomechanical parameters of pea gravel; s3, constructing three-dimensional models of duct pieces, surrounding rocks, hydraulic reclamation pipes and the tail part of the shield tunneling machine; s4, constructing a pea gravel hydraulic reclamation pressure implementation model; and S5, realizing the model by utilizing the pea gravel hydraulic reclamation pressure, and realizing simulation of the pea gravel hydraulic reclamation process of the single-ring duct piece in a step-by-step hydraulic reclamation mode. The device can simulate the process that pea gravels are continuously blown into gaps, is suitable for research on simulation of mechanical behaviors of discrete granular materials, and has important practical value.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of geotechnical engineering and tunnel engineering, and particularly relates to a simulation method and system for a pea gravel filling process based on discrete elements and application thereof. BACKGROUND

[0002] A full-face tunnel boring machine (TBM for short) is widely used in various underground projects such as traffic tunnels, coal mine projects and water diversion projects due to its high construction speed and reliability. When segments are used as a supporting structure in a TBM tunnel, a gap of about 5-20 cm exists between the segments and the surrounding rock, which is usually filled with pea gravel and then grouted to form a pea gravel grouting layer of the surrounding rock and a stable stress system of the segments. Before pea gravel grouting, the pea gravel has poor mechanical parameters, is loose and changeable, and has limited constraints on the segments. Meanwhile, the gap behind the segment wall is a closed space, and the pea gravel filling is not always dense and uniform due to the influence of construction technology and geological conditions. A large amount of engineering experience shows that when the pea gravel filling is not dense and uniform, stress concentration easily occurs in the segments, which leads to large-scale segment misalignment, cracking and water leakage in the tunnel. Therefore, it is necessary to investigate the distribution characteristics of the pea gravel behind the segment wall to study the stress of the segments.

[0003] In recent years, numerical methods based on the mechanics of discontinuous media, represented by the discrete element method (DEM), have been widely used in numerical simulation research on discrete materials. Among them, the commercial software Particle Flow Code (PFC) for particle modeling has high flexibility in programming implementation, and is therefore widely used in numerical simulation of particle systems. SUMMARY

[0004] The present application aims to overcome the deficiencies of the prior art, and provides a simulation method and system for a pea gravel filling process based on discrete elements and application thereof. The method can restore the process of pea gravel being blown into the gap between the surrounding rock and the segments through a blowing pipe and continuously accumulating in the gap to the greatest extent, so that the distribution of the pea gravel in the gap can be analyzed and defects in the pea gravel backfill layer can be found out.

[0005] The present application adopts the following technical solutions:

[0006] In one aspect, the present application provides a simulation method for a pea gravel filling process based on discrete elements, comprising:

[0007] S1, determining the size parameters of a shield tunnel, the macroscopic mechanical parameters of segments and surrounding rock, a filling position and a filling pressure;

[0008] S2, determining the particle size distribution of pea gravel particles and the mesoscopic mechanical parameters of the pea gravel;

[0009] S3, constructing a three-dimensional model of the segment, the surrounding rock, the filling pipe and the tail of the shield tunnel machine according to the size parameters of the shield tunnel in step S1;

[0010] S4, constructing a granular soil filling pressure implementation model according to the filling pressure in step S1, the particle size distribution of the gravel particles and the mesoscopic mechanical parameters of the pea gravel in step S2, and the three-dimensional model in step S3;

[0011] S5, implementing a single-ring segment pea gravel filling process simulation in a step-by-step filling manner by using the pea gravel filling pressure implementation model obtained in step S4;

[0012] Steps S1 and S2 have no sequence.

[0013] According to any possible implementation manner described above, further provided is an implementation manner, in step S1, the size parameters of the shield tunnel include an outer diameter of the shield tunnel machine, an outer diameter of the segment, an inner diameter of the segment, a width of the segment and a diameter of the filling hole.

[0014] The macroscopic mechanical parameters of the segment include an elastic modulus of the segment, a friction coefficient of the segment and a density of the segment.

[0015] The macroscopic mechanical parameters of the surrounding rock include an elastic modulus of the surrounding rock, a friction coefficient of the surrounding rock and a density of the surrounding rock.

[0016] The filling position is a position of the pea gravel filling hole; and the filling pressure is a pushing force of the air blower on the pea gravel in the filling pipe during the pea gravel filling.

[0017] According to any possible implementation manner described above, further provided is an implementation manner, in step S2, the particle size distribution of the pea gravel particles is measured through a sieve separation experiment; a plurality of standard square-hole sieves with different nominal diameters are used, the square-hole sieves are arranged from top to bottom in the order of decreasing sieve hole diameter before sieving, and the sieving is stopped when the particle passing amount per minute does not exceed a set proportion of the total amount of the sample.

[0018] The mesoscopic mechanical parameters of the pea gravel include density, effective modulus, normal-tangential stiffness ratio, particle friction coefficient and damping coefficient; the mesoscopic mechanical parameters of the pea gravel are obtained through a triaxial shear simulation test, three levels of confining pressures are set in the range of 50kPa-200kPa, a stress-strain curve is obtained through sample formation, pre-pressing, confining pressure application and axial force application monitoring, and a linear contact model is selected for the contact model of the pea gravel.

[0019] The porosity of the pea gravel accumulation body is obtained through a stacking experiment, which is simulated in PFC by using a method of sequentially adding particles layer by layer under the action of gravity.

[0020] Any possible implementation manner as described above is further provided with an implementation manner, the packing experiment is specifically: a cuboid container with an upper opening is surrounded by using wall units in PFC, and particles are generated above the area; the particle generation is performed in multiple times, one layer is generated each time, each layer is composed of a particles with random positions and directions, and each layer is generated every t(s), a total of b layers of particles, a and b are natural numbers; the distance between the generation position of the particle layer and the bottom of the container is twice the height of the container; the particle size range adopts the result of the screening experiment in step S2, and the contact parameters between the particles are selected from the contact parameters calibrated in the triaxial shear simulation experiment in step S2; when the average particle velocity is lower than 1*10 -4 m / s, it is considered that the packing reaches stability, and the porosity of the particle packing body is measured by using a measuring circle.

[0021] Any possible implementation manner as described above is further provided with an implementation manner, in step S3, the segment, the surrounding rock and the blow pipe are all composed of surface units, a three-dimensional model is established in Solidworks or Rhino modeling software, and the format is converted into an stl file recognized by PFC, the stl file is imported into PFC, and the segment, the surrounding rock, the blow pipe and the tail of the shield machine are reconstructed by using wall units.

[0022] Any possible implementation manner as described above is further provided with an implementation manner, in step S4, the construction of the pea gravel blow pressure implementation model is specifically:

[0023] S41, according to the minimum particle size and the maximum particle size of the pea gravel measured in step 2, the porosity e of the pea gravel particles under the condition of random free fall packing is obtained by sample preparation, packing and porosity ratio monitoring, which is used for the calculation of the servo speed in S45;

[0024] S42, the pushing force F received by the pea gravel in the blow pipe is determined, and the calculation formula of F is: F=pA(1-e), wherein p is the blow pressure, A is the blow hole area, and e is the porosity;

[0025] S43, the pushing force F received by the pea gravel in the blow pipe in step S42 is set as the target servo force;

[0026] S44, a square wall unit (hereinafter referred to as wall1) is generated, and the motion direction thereof is set to be perpendicular to the pea gravel blow pipe; the sum of the effective translational stiffness of all contacts on wall1 at each time and the current time step are counted, and are denoted as K c and Δt respectively; the servo coefficient is calculated by the following formula: In the formula, alpha is a stress relaxation factor;

[0027] S45, monitor the contact force between wall1 and the gravel particles, multiply the difference between the current contact force and the target servo force set in step 3.3 by the servo coefficient obtained in step 3.4 to obtain the servo speed of wall1, and the calculation formula is: v n = G (F t +F c ), wherein F t and F c are the target servo force and the current contact force, respectively;

[0028] S46, set the servo speed obtained in step S45 as the movement speed of wall1 along the direction of the blow pipe, so as to push the gravel in the blow pipe, and change the movement speed in real time according to the size of the current pushing force.

[0029] According to any possible implementation manner described above, further provided is an implementation manner, in step S5, the specific steps of the step-by-step blow filling to realize the simulation of the single-ring pipe piece gravel blow filling process are as follows:

[0030] S51, push the blow pipe, the tail of the shield machine and wall1 to the current pipe piece position along the direction of the advance of the shield machine; generate part of the gravel in the blow pipe, and assign the contact parameters and the mass to the part of the gravel;

[0031] S52, clear the speed of the gravel in the blow pipe every 50-100 cycles, and continue such operation for about 10 times; each cycle is the minimum unit of time advance in PFC, and the model state is updated every cycle; the contact force in PFC is realized based on the overlap between the particles, and the particles often overlap with each other when generated, at this time, a larger contact force is generated between the particles, so that the particle movement is out of control, therefore, the purpose of this step is to make the newly generated gravel particles spread out at a slower speed, reduce the overlap between the particles, and maintain the stability of the particle accumulation body;

[0032] S53, control wall1 to push the gravel along the blow pipe forward using the servo speed determined in step S4;

[0033] S54, when wall1 moves to the end of the blow pipe, reset wall1, and prepare for the next blow filling;

[0034] S55, repeat the process of steps S51-S54, and continuously push the gravel particles into the gap model between the surrounding rock and the pipe piece in batches;

[0035] S56, continuously monitor the movement speed of wall1 during the blow filling process, stop moving wall1 when the termination filling condition is reached, delete the gravel in the blow pipe, reset the blow pipe, and generate a new wall unit (named as wall2) at the blow hole position as a sealing, and the annular gap blow filling is completed;

[0036] S57, pushing the pipe, the tail of the shield machine and the wall 1 to the next ring segment position in the advancing direction of the shield machine, repeating steps S51-S56 until the filling of the gap behind all segments is completed.

[0037] According to any possible implementation manner described above, further provided is an implementation manner, in step S5, the filling termination condition in step S56 is that the movement speed of the wall 1 is less than a certain set value, the set value is between 1x10 -6 m / s and 1x10 -3 m / s, which indicates that the movement of the pea gravel in the pipe is close to stagnation.

[0038] In another aspect, the present application also provides a pea gravel filling process simulation system based on discrete elements, which is used to implement the method described above, and the system comprises:

[0039] A shield tunnel parameter determination unit is configured to determine the size parameters of the shield tunnel, the macro-mechanical parameters of the segments and the surrounding rock, the filling position and the filling pressure.

[0040] A pea gravel parameter determination unit is configured to determine the particle size distribution of the pea gravel particles and the meso-mechanical parameters of the pea gravel.

[0041] A pea gravel filling pressure implementation model construction unit is configured to construct a pea gravel filling pressure implementation model according to the relevant parameters determined by the shield tunnel parameter determination unit and the pea gravel parameter determination unit.

[0042] A pea gravel filling process simulation unit is configured to simulate the pea gravel filling process using the pea gravel filling pressure implementation model.

[0043] In another aspect, the present application also provides an application of the pea gravel filling process simulation method based on discrete elements described above, for a specific shield tunnel, different particle size distributions of pea gravel particles, different filling positions and different filling pressures are selected, the pea gravel filling process is simulated respectively using the pea gravel filling pressure implementation model, and the filling simulation results are obtained; the filling simulation results are evaluated, and at least one of the three factors of the particle size distribution of the pea gravel particles, the filling position and the filling pressure is optimized according to the filling simulation results, and the optimized actual construction parameters are obtained.

[0044] The present application has the following beneficial effects:

[0045] 1. The granular material filling process simulation method based on discrete elements can be popularized to other granular material filling processes. Based on the step-by-step filling method, granular particles are generated in batches, and the granular material is blown into the gap between the segment and the surrounding rock at a fixed pressure. This method can study the effects of blowing pressure and blowing position on the granular material blowing speed and the compaction degree of the granular material filling layer. The present application does not need to generate a large number of particles in advance, avoiding the waste of computing resources and greatly improving the computing efficiency. Physical model tests often cost a lot in terms of cost-effectiveness and time. The discrete element simulation can not be limited by laboratory tests, and has flexibility and expandability. The discrete element simulation can supplement some experiments, and the granular material filling process simulation method based on discrete elements can obtain data that are not easy to measure in real tunnels. Through analysis of the data, the compaction degree of the filling layer behind the wall can be further monitored.

[0046] 2. The granular material filling process simulation method based on discrete elements is an important method for studying the granular material filling mechanism, and the blowing pressure in the granular material filling process is simulated through the servo principle. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 The figure shows a granular material filling process simulation flowchart based on discrete elements.

[0048] Figure 2 The figure shows a triaxial test simulation diagram for granular material contact parameter calibration in the example.

[0049] Figure 3 The figure shows the stress-strain curve for granular material contact parameter calibration in the example.

[0050] Figure 4 The figure shows a wall unit schematic diagram in the example.

[0051] Figure 5 The figure shows a granular material accumulation experiment diagram in the example.

[0052] Figure 6 The figure shows a granular material grading curve in the example.

[0053] Figure 7 The figure shows a segment number and blowing hole position schematic diagram in the example.

[0054] Figure 8 The figure shows the position and number of monitoring points in the filling model.

[0055] Figure 9 The figure shows a porosity change diagram in the example.

[0056] Figure 10 A graph of the change of the blowing pressure in the embodiment is shown.

[0057] Figure 11 A graph of the change of the servo speed in the embodiment is shown.

[0058] Figure 12 A graph of the particle size distribution of the pea gravel in the embodiment is shown.

[0059] Figure 13 A graph of the particle distribution of the pea gravel in the embodiment is shown. DETAILED DESCRIPTION

[0060] Hereinafter, specific embodiments of the present application will be described in detail with reference to the accompanying drawings. It should be noted that the technical features described in the following embodiments should not be considered in isolation, and they can be combined with each other to achieve better technical effects.

[0061] As Figure 1 shown, an embodiment of the present application is a pea gravel blowing process simulation method based on discrete elements, comprising:

[0062] S1, determining the size parameters of the shield tunnel, the macro-mechanical parameters of the segment and the surrounding rock, the blowing position and the blowing pressure;

[0063] S2, determining the particle size distribution of the pea gravel and the meso-mechanical parameters of the pea gravel; the PFC model for calibrating the contact mechanical parameters of the pea gravel is as shown in Figure 2 , and the calibration results are as shown in Figure 3

[0064] S3, constructing a three-dimensional model of the segment, the surrounding rock, the blowing pipe and the tail of the shield machine according to the size parameters of the shield tunnel in step S1;

[0065] S4, constructing a pea gravel blowing pressure implementation model according to the blowing pressure in step S1, the particle size distribution of the gravel and the meso-mechanical parameters of the pea gravel in step S2, and the three-dimensional model in step S3;

[0066] S5, realizing the simulation of the pea gravel blowing process of the single-ring segment by the step-by-step blowing method using the pea gravel blowing pressure implementation model obtained in step S4;

[0067] Steps S1 and S2 have no sequence.

[0068] In one specific embodiment, the size parameters of the shield tunnel include the outer diameter of the shield machine, the outer diameter of the segment, the inner diameter of the segment, the width of the segment and the diameter of the blowing hole.

[0069] ​The macro-mechanical parameters of the pipe segment include an elastic modulus of the pipe segment, a friction coefficient of the pipe segment, and a pipe segment density;

[0070] The macro-mechanical parameters of the surrounding rock include an elastic modulus of the surrounding rock, a friction coefficient of the surrounding rock, and a surrounding rock density;

[0071] The filling position is a position of the pea gravel filling hole, and the filling pressure is a pushing force of the air blower on the pea gravel in the filling pipe during pea gravel filling.

[0072] In one specific embodiment, in step S2, the particle size distribution of the pea gravel is measured by a sieving experiment; a plurality of standard square-hole sieves with different nominal diameters are used, the square-hole sieves are arranged from top to bottom in the order of decreasing hole diameter before sieving, and then placed on a vibrating sieve machine for sieving, and the sieving is stopped when the particle passing amount per minute is less than a set proportion (for example, 0.1%) of the total amount of the sample.

[0073] In one specific embodiment, the sieving experiment of the pea gravel uses a quartering method to select the sample, and national standard square-hole sieves with nominal diameters of 2.5 mm, 5 mm, 10 mm, and 16 mm are used.

[0074] In one specific example, the pea gravel grading curve obtained through the sieving experiment is as shown in Figure 6 .

[0075] In one specific embodiment, the meso-mechanical parameters of the pea gravel include density, effective modulus, normal-tangential stiffness ratio, particle friction coefficient, and damping coefficient; the meso-mechanical parameters of the pea gravel are obtained by a triaxial shear simulation test, three levels of confining pressures are set in the range of 50 kPa to 200 kPa, and a stress-strain curve is obtained by sample formation, pre-pressing, confining pressure application, and axial force application monitoring, and a linear contact model is selected for the contact model of the pea gravel.

[0076] In one specific embodiment, the contact parameter settings are as shown in Table 1:

[0077] Table 1 Contact parameter table

[0078]

[0079] In one specific embodiment, the porosity of the pea gravel accumulation body is obtained by a stacking experiment, which is performed in PFC by sequentially adding particles layer by layer, and the simulation particles are only naturally stacked under the action of gravity.

[0080] In one specific embodiment, the stacking experiment is specifically: using wall units in PFC to enclose an open-top cubic container, and generating particles above the area; the particle generation is performed in multiple times, each time generating a layer, each layer consisting of a particles with random position and direction, and generating a layer every t(s), a total of b layers of particles; the distance between the generation position of the particle layer and the bottom of the container is twice the height of the container; the particle size range adopts the result of the sieving experiment in step S2, and the contact parameters between the particles are selected from the contact parameters calibrated in the triaxial shear simulation experiment in step S2; when the average particle velocity is lower than 1×10 -4 m / s, it is considered that the stacking reaches stability, and the porosity of the particle stacking body is measured using a measuring circle.

[0081] In one specific embodiment, the segment, surrounding rock and dredged pipe are all constructed using surface units, a three-dimensional model is established in Solidworks or Rhino modeling software, and the format is converted into a stl file recognized by PFC, and the stl file is imported into PFC, and the wall unit is used to reconstruct the segment, surrounding rock, dredged pipe and shield tail.

[0082] In one specific embodiment, for a certain actual engineering project, the shield diameter d1=9.5m, the lining segment outer radius d2=9.1m, the segment width l=1.6m, and the gap width between the segment and the surrounding rock is t=0.2m, and each ring segment has two symmetrically distributed dredging holes, as shown in Figure 7 The model size is scaled by 4:1 in the circumferential and axial directions with reference to the actual size of the project, and three ring segments are taken as the research object. The surrounding rock excavation inner radius is set to D1=1.2m, the segment outer radius D2=1m, and the segment width L=0.4m. The gap width between the segment and the surrounding rock remains 0.2m unchanged. The finally constructed segment-surrounding rock gap model is shown in Figure 4 The model size parameters are shown in Table 2:

[0083] Table 2 Model size parameter table

[0084]

[0085] In one specific embodiment, in step S4, the construction of the pea gravel dredging pressure implementation model is specifically:

[0086] S41, according to the minimum particle size and the maximum particle size of the pea gravel measured in step 2, the porosity of the pea gravel particles under the condition of random free fall stacking is obtained by sample preparation, stacking and porosity ratio monitoring, as shown in Figure 5 for the calculation of the servo speed in S45;

[0087] S42, calculate the pushing force of the pea gravel in the pipe based on the following three assumptions: 1) the pushing force of the air flow only acts on the pea gravel in the pipe; 2) the friction between the air and the surface of the pea gravel is ignored; 3) the pores of the pea gravel in the pipe are connected, and the air can pass through the pores freely;

[0088] The calculation formula of the pushing force F of the pea gravel in the pipe is: F = pA (1-e), wherein p is the filling pressure, A is the filling hole area, and e is the porosity;

[0089] S43, set the pushing force F of the pea gravel in the pipe in step S42 as the target servo force;

[0090] S44, generate a square wall (named wall1) unit, set its movement direction perpendicular to the pea gravel pipe, and set the area of the wall1 greater than the area of the pea gravel pipe, so that the projection of the pipe model on the wall1 does not exceed the wall1; count the sum of the effective translational stiffness of all contacts on the wall1 at each time and the current time step, and record them as K c and Δt respectively; calculate the servo coefficient by the following formula: In the formula, α is the stress relaxation factor;

[0091] S45, monitor the contact force between the wall1 and the pea gravel particles, multiply the difference between the current contact force and the target servo force set in step 3.3 by the servo coefficient obtained in step 3.4 to obtain the servo speed of the wall1, and the calculation formula is: v n =G (F t +F c ), wherein F t and F c are the target servo force and the current contact force respectively;

[0092] S46, set the servo speed obtained in step S45 as the movement speed of the wall1 in the direction of the pipe, so as to push the pea gravel in the pipe, and change the movement speed in real time according to the size of the current pushing force.

[0093] The positions and numbers of the monitoring points in the model are shown in Figure 8 , and the porosity changes of some monitoring points are recorded in Figure 9The porosity of the first ring at each position is significantly improved when the second ring gap is started to be filled, which indicates that the particles of the first ring start to fall into the gap of the second ring. The loss at positions a4 and a6 is larger, while the loss at positions a8 and a10 is smaller. When the third ring gap is filled, the porosity of the second ring changes in the same way, and the porosity of the first ring is also affected, but the change range is smaller. The porosity of a12 and b12 at each ring filling completion gradually decreases, indicating that the compaction degree at the bottom of the gap gradually increases as the filling process proceeds.

[0094] The wall1, i.e. the normal contact force on the filling pressure, is regarded as the current servo force, and the servo force and servo speed during the filling process are counted. As shown in Figure 10 The change curve of the servo force is divided into several intervals, and the force at both ends of each interval is 0. The number of these intervals is equal to the filling number of the ring, and each interval represents a complete filling process. Due to the inertia of the pea gravel, a higher servo force is generated when wall1 first contacts the particles, and then the servo force gradually decreases as the speed of the pea gravel increases. When the filling number is less than 12, the surrounding rock-tube gap is not filled with pea gravel, and the servo force does not change much; when the filling number is greater than 12, the pea gravel has been accumulated near the filling port, and the newly blown pea gravel particles are resisted by the pea gravel in the gap, so the movement speed slows down and the servo force increases. As the filling continues, the gap is filled with pea gravel particles, and the servo force gradually approaches the set target servo force. When the servo force reaches the target servo force and the wall1 movement speed approaches 0, the filling stops. Figure 11 For the servo speed change, the process is also divided into several intervals, and the dividing point between the intervals is a fixed peak value, which can be calculated.

[0095] In one embodiment, in step S5, the specific steps of the step-by-step filling to realize the simulation of the pea gravel filling process of the single-ring segment are as follows:

[0096] S51, push the filling pipe, the tail of the shield machine and wall1 to the current segment position in the direction of the advance of the shield machine; generate part of the pea gravel in the filling pipe, and give it contact parameters and mass;

[0097] The number of pea gravel generated in the filling pipe each time should be 1 / 20-1 / 50 of the total amount of pea gravel calculated for filling the ring. The particle generation parameters are shown in Table 3.

[0098] Table 3 Particle generation parameter table

[0099]

[0100] S52: Reset the velocity of the pea gravel in the filling tube to zero every 50 to 100 cycles, and continue this operation for about 10 times. Each cycle is the smallest unit of time advancement in PFC, and each cycle updates the model state. In PFC, contact force is based on the overlap between particles. When particles are generated, they often overlap, which generates large contact forces between particles and causes uncontrolled particle movement. Therefore, the purpose of this step is to allow the newly generated pea gravel particles to disperse at a slower speed, reduce the overlap of particles, and maintain the stability of the particle accumulation.

[0101] S53, using the servo speed determined in step S4 to control wall1 to push the pea gravel forward along the blow-fill pipe;

[0102] S54, when wall 1 moves to the end of the blowing and filling pipe, wall 1 is reset to prepare for the next blowing and filling;

[0103] S55, repeating steps S51-S54 to continuously push the pea gravel particles into the gap model between the surrounding rock and the segment in batches;

[0104] S56: During the filling process, the movement speed of wall 1 is continuously monitored. When the filling termination condition is reached, the movement of wall 1 is stopped, the pea gravel in the filling pipe is deleted, and the filling pipe is reset. A new wall unit (named wall 2) is generated at the filling hole position as a plug, and the filling of the annulus is completed. The mechanical parameters of wall 2 at the filling hole position are set to be consistent with those of the pipe segment.

[0105] S57: Push the filling pipe, the tail of the shield machine and wall1 to the position of the next ring segment along the forward direction of the shield machine, and repeat steps S51-S56 until the gaps behind all the segments are filled.

[0106] Figure 12 The image shows the particle size distribution of the pea gravel after filling. The particle size increases gradually from blue to red. It can be seen that at the bottom of the void, coarse particles accumulate on the segment side, while fine particles accumulate on the surrounding rock side. In the upper middle of the void, coarse particles accumulate on the surrounding rock side, while fine particles accumulate on the segment side.

[0107] Figure 13 This image shows the distribution of pea gravel particles as the shield machine advances. Blue corresponds to particles blown into the first ring, while green and red correspond to particles blown into the second and third rings, respectively. After each ring gap is filled, the rear end of the shield machine advances, and the previously accumulated pea gravel falls into the newly created gap, forming cavities in the upper fill layer. These cavities are then filled by newly blown pea gravel, resulting in a skewed distribution of pea gravel between batches.

[0108] In step S5, the filling termination condition in step S56 is that the movement speed of wall 1 is less than a certain set value, which is between 1 x 10 -6 m / s and 1 x 10 -3 m / s, indicating that the movement of the pea gravel in the filling pipe is close to stagnation.

[0109] The method of the present application can simulate the process of continuously filling pea gravel into the gap, is suitable for simulating the mechanical behavior of granular materials, and has important practical value.

[0110] Although several embodiments of the present application have been given in the present text, those skilled in the art should understand that the embodiments in the present text can be changed without departing from the spirit of the present application. The above embodiments are only exemplary and should not be taken as a limitation on the scope of the present application.

Claims

1. A pea gravel filling process simulation method based on discrete element method, characterized in that: The method comprises: S1. Determine the dimensional parameters of the shield tunnel, the macroscopic mechanical parameters of the segments and surrounding rock, the filling position and pressure; S2. Determine the particle size distribution of pea gravel particles and the micromechanical parameters of pea gravel; S3. Constructing a three-dimensional model of the segments, surrounding rock, blown-fill pipe, and the tail of the shield machine based on the dimensional parameters of the shield tunnel in step S1; S4. Constructing a pea gravel filling pressure realization model based on the filling pressure of step S1, the particle size distribution of gravel particles and the micromechanical parameters of pea gravel in step S2, and the three-dimensional model of step S3; S5. Using the pea gravel filling pressure model obtained in step S4, a simulation of the pea gravel filling process of a single ring segment is realized by step-by-step filling. There is no order for steps S1 and S2.

2. The discrete element-based pea gravel filling process simulation method according to claim 1, characterized in that: In step S1, the dimension parameters of the shield tunnel include the outer diameter of the shield machine, the outer diameter of the segment, the inner diameter of the segment, the width of the segment and the diameter of the blow-fill hole; The macroscopic mechanical parameters of the segment include the elastic modulus, friction coefficient and density of the segment; The macroscopic mechanical parameters of surrounding rock include elastic modulus, friction coefficient and density of surrounding rock; The filling position refers to the location of the pea gravel filling hole; the filling pressure refers to the thrust exerted by the blower on the pea gravel in the filling pipe when filling the pea gravel.

3. The pea gravel filling process simulation method based on discrete element method according to claim 1, characterized in that: In step S2, the particle size distribution of the pea gravel particles is measured by a sieving experiment; using several standard square sieves of different nominal diameters, the square sieves are arranged from top to bottom in descending order of sieve aperture before sieving, and sieving is stopped when the particle throughput per minute does not exceed a set proportion of the total sample volume; The micromechanical parameters of the pea gravel include density, effective modulus, normal-tangential stiffness ratio, particle friction coefficient, and damping coefficient. The micromechanical parameters of the pea gravel are obtained through a triaxial shear simulation test. Three levels of confining pressure are set within the range of 50 kPa to 200 kPa. Stress-strain curves are obtained by sampling, preloading, applying confining pressure, and applying axial force monitoring. The contact model of the pea gravel is a linear contact model. The porosity of the pea gravel accumulation was measured by stacking experiments, which were performed in a PFC using a layer-by-layer sequential addition of particles to simulate the natural accumulation of particles under the influence of gravity alone.

4. The discrete element-based pea gravel filling process simulation method according to claim 3, characterized in that: The stacking experiment is as follows: a cubic container with an open top is formed in the PFC using wall units, and particles are generated above the area; particle generation is performed multiple times, with each layer being generated, each layer consisting of a particles with random positions and orientations, and a layer is generated every t(s), with b layers of particles coexisting, where a and b are both natural numbers; The distance between the particle layer generation position and the bottom of the container is twice the height of the container; the particle size range is based on the results of the screening experiment in step S2, and the contact parameters between the particles are based on the contact parameters calibrated by the triaxial shear simulation experiment in step S2; when the average particle velocity is less than 1×10 -4 When the particle size reaches 0.05477 Å / s, the accumulation is considered to be stable, and the porosity of the particle accumulation is measured using a measuring circle.

5. The pea gravel filling process simulation method based on discrete element method according to claim 1, characterized in that: In step S3, the segments, surrounding rock, and blown-fill pipe are constructed using surface elements. A 3D model is created in Solidworks or Rhino modeling software and converted into an STL file recognized by PFC. The STL file is imported into PFC, and the segments, surrounding rock, blown-fill pipe, and shield machine tail are reconstructed using wall elements.

6. The method for simulating pea gravel filling process based on discrete element method according to claim 1, characterized in that: In step S4, the pea gravel filling pressure realization model is constructed as follows: S41, based on the minimum and maximum particle sizes of the pea gravel measured in step 2, the porosity of the pea gravel particles under random free-fall accumulation is obtained by sampling, stacking and porosity monitoring. e ; S42. Determine the thrust F exerted on the pea gravel in the filling pipe. The calculation formula for F is: F = pA(1-e), where p is the filling pressure and A is the filling hole area. e is the porosity; S43, setting the thrust F exerted on the pea gravel in the blowing and filling pipe in step S42 as the target servo force; S44. Generate a square wall unit, name it wall1, and set its movement direction to be perpendicular to the pea gravel blowing pipe; The sum of the effective translation stiffness of all contacts on wall1 at each moment and the current time step are counted and recorded as K c and Δt; the servo coefficient is calculated by the following formula: Where α is the stress relaxation factor; S45, monitor the contact force between wall1 and the pea gravel particles, multiply the difference between the current contact force and the target servo force set in step 3.3 by the servo coefficient obtained in step 3.4 to obtain the servo speed v n , and its calculation formula is: v n =G(F t +F c ), where Ft and Fc are the target servo force and the current contact force respectively; S46. Set the servo speed obtained in step S45 as the movement speed of wall1 along the blowing and filling pipe, so that it pushes the pea gravel in the blowing and filling pipe, and changes the movement speed in real time according to the current thrust.

7. The method for simulating pea gravel filling process based on discrete element method according to claim 1, characterized in that: In step S5, the specific steps for simulating the single-ring segment pea gravel filling process by step-by-step filling are as follows: S51, advancing the blow-fill pipe, the tail of the shield machine, and wall1 to the current segment position along the forward direction of the shield machine; generating some pea gravel in the blow-fill pipe, and assigning contact parameters and mass to it; S52. Reset the velocity of the pea gravel in the filling tube to zero every 50 to 100 cycles, and continue this operation for about 10 times. Each cycle is the smallest unit of time advancement in PFC, representing a complete calculation cycle. In PFC, contact force is based on the overlap between particles. When particles are generated, they often overlap, generating large contact forces between particles, causing uncontrolled particle movement. The purpose of this step is to allow the newly generated pea gravel particles to disperse at a slower rate, reduce the overlap of particles, and maintain the stability of the particle accumulation. S53, using the servo speed determined in step S4 to control wall1 to push the pea gravel forward along the blow-fill pipe; S54, when wall 1 moves to the end of the blowing and filling pipe, wall 1 is reset to prepare for the next blowing and filling; S55, repeating steps S51-S54 to continuously push the pea gravel particles into the gap model between the surrounding rock and the segment in batches; S56: During the filling process, the movement speed of wall 1 is continuously monitored. When the filling termination condition is reached, the movement of wall 1 is stopped, the pea gravel in the filling pipe is deleted, and the filling pipe is reset. A new wall unit is generated at the filling hole position as a plug, and the gap of the ring is filled. S57: Push the filling pipe, the tail of the shield machine and wall1 to the position of the next ring segment along the forward direction of the shield machine, and repeat steps S51-S56 until the gaps behind all the segments are filled.

8. The discrete element-based pea gravel filling process simulation method according to claim 7, characterized in that: The filling termination condition in step S56 is: the speed of wall1 is less than a certain set value, which is within the range of 1×10 -6 m / s to 1×10 -3 m / s, indicating that the movement of pea gravel in the blow-fill pipe is close to stagnation.

9. A pea gravel filling process simulation system based on discrete element method, characterized in that: The system is used to implement the method according to any one of claims 1 to 8, and the system includes: Shield tunnel parameter determination unit, used to determine the shield tunnel's dimensional parameters, macroscopic mechanical parameters of the segments and surrounding rock, filling position, and filling pressure; Pea gravel parameter determination unit, used to determine the particle size distribution of pea gravel particles and the microscopic mechanical parameters of pea gravel; A pea gravel filling pressure realization model construction unit is configured to construct a pea gravel filling pressure realization model based on relevant parameters determined by the shield tunnel parameter determination unit and the pea gravel parameter determination unit; The pea gravel filling process simulation unit uses the pea gravel filling pressure realization model to simulate the pea gravel filling process.

10. An application of the discrete element-based pea gravel filling process simulation method according to any one of claims 1 to 8, characterized in that: For a specific shield tunnel, different pea gravel particle size distributions, different filling locations, and different filling pressures are selected. The pea gravel filling process is simulated using a pea gravel filling pressure realization model to obtain filling simulation results. The filling simulation results are evaluated, and based on the filling simulation results, at least one of the three factors, pea gravel particle size distribution, filling location, and filling pressure, is optimized to obtain the optimized actual construction parameters.

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